Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles\' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

Coronavirus pandemic has been a huge jeopardy to human health in various systems since it outbroke, early detection and prevention of further escalation has become a priority. The current popular approach is to collect samples using the nasopharyngeal swab method and then test for RNA using the real-time polymerase chain reaction, which suffers from false-positive results and a longer diagnostic time scale. Alternatively, various optical techniques, namely, optical sensing, spectroscopy, and imaging shows a great promise in virus detection. In this mini review, we briefly summarize the development progress of vibrational spectroscopy techniques and its applications in the detection of SARS-CoV family. Vibrational spectroscopy techniques such as Raman spectroscopy and infrared spectroscopy received increasing appreciation in bio-analysis for their speediness, accuracy and cost-effectiveness in detection of SARS-CoV. Further, an account of emerging photonics technologies of SARS-CoV-2 detection and future possibilities is also explained. The progress in the field of vibrational spectroscopy techniques for virus detection unambiguously show a great promise in the development of rapid photonics-based devices for COVID-19 detection.

In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients\' prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment.

Deep fried foods are popular among consumers due to their unique taste and texture. During the process of deep-frying, oil is subjected to a high temperature that results into the generation of harmful compounds. The repeated usage of frying oil is a common exercise and associated with various health hazards. Thus, determination of frying oil quality is a critical practice to follow. The chemical methods employed to determine the quality of frying oil are destructive and require large amount of harmful chemical, thus researchers are exploring the application of various vibrational spectroscopic techniques for this purpose. The first part of this review provides a detailed insight into fundamental theoretical aspects of two main vibrational spectroscopic techniques (infrared and Raman spectroscopy) and chemical alteration in frying oils under thermal stress. While in the following parts, the application of near-infrared (NIR) and Fourier transform infrared (FTIR) and Raman spectroscopy for evaluating the quality of various frying oils and fats under thermal stress has been discussed. It is anticipated that this review paper can serve as a reference source for impending research in this field.

The dating of organic findings is a fundamental task for many scientific fields. Radiocarbon dating is currently the most commonly used method. For wood, dendrochronology is another state-of-the-art method. Both methods suffer from systematic restrictions, leading to samples that have not yet been able to be dated. Molecular changes over time are reported for many materials under different preservation conditions. Many of them are intrinsically monotonous. These monotonous molecular decay (MD) patterns can be understood as clocks that start at the time when a given molecule was formed. Factors that influence these clocks include input material composition and preservation conditions. Different wood species, degrees of pyrolysis, and pretreatments lead to different prediction models. Preservation conditions might change the speed of a given clock and lead to different prediction models. Currently published models for predicting the age of wood, paper, and parchment depend on infrared spectroscopy. In contrast to radiocarbon dating, dating via MD does not comprise a single methodology. Some clocks may deliver less precise results than the others. Ultimately, developing a completely different, new dating strategy-such as MD dating-will help to bring to light a treasure trove of information hidden in the darkness of organic findings.

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.

Chemical compositions of mushrooms are greatly dependent on the geographical region, and also the different parts of the same mushroom have different chemical constitutions. Several chemical methods are employed for quality control of mushrooms. However, these methods are destructive, require skilled personnel and are time consuming. To overcome these limitations researchers are aiming for vibrational spectroscopic techniques. This review is focused on various studies related to the application of vibrational spectroscopy for classification, authentication and quality analysis of mushrooms. It was concluded that vibrational spectroscopy could be efficiently employed for assessing the quality, authenticity and geographical origin of the mushrooms. Fourier-transform infrared (FTIR) and near infrared (NIR) spectroscopy were the most explored, whereas, Raman spectroscopy is the least explored technique in this field. Compact and cost-effective spectrometers based on the selective wavelengths have to be designed and installed at commercial and industrial level for rapid quality control of mushrooms.